This study examines the differential effects of three dimensions of dynamic capabilities on innovation performance and investigates how ambidextrous learning (exploratory and exploitative learning) moderates these relationships. Drawing on survey data from 299 Chinese enterprises, we employ hierarchical regression analysis with interaction terms to test the proposed hypotheses. The results reveal that all three capability dimensions significantly enhance innovation performance, with innovative capacity exerting the strongest effect. Both exploratory and exploitative learning positively moderate the capability–performance relationships, though their effects vary across capability dimensions: exploitative learning more strongly reinforces the effect of absorptive capacity, while exploratory learning more effectively amplifies the influence of innovative capacity. These findings contribute to the dynamic capabilities literature by revealing the heterogeneous performance implications of distinct capability dimensions and demonstrating how different learning modes serve as boundary conditions that shape capability effectiveness.
Ma et al. (2026) studied this question.